The popularity of digital media and the Internet has resulted in easy access to extremely large volumes of digital media, including images, video, audio, and the like. Accurate identification of such media is important for effective searching; that is, search results are dependent on the quality of media identification. Digital media is typically identified or classified using media annotations or keywords, commonly known as “tags”. Methods of “tagging” media tend to be tedious and expensive, not well-suited to automation. Manual tagging—tagging by a human being—often results in the highest quality tags. But the sheer volume of digital media makes manual tagging a challenge. Social tagging (a form of manual tagging involving groups of people, also known as folksonomy, collaborative tagging, social classification, social indexing, and the like), the practice of collaboratively tagging digital media, can be advantageous. However, noisy tags frequently appear and users typically do not tag all semantic elements in the media.
Noise with respect to media tagging generally refers to inaccurate or misleading identification of media, typically due to low-quality tags. Noise may result from synonyms (multiple tags for the same media, e.g., “sea” and “ocean”), homonymy (same tag with different meanings, e.g., “apple” the fruit and “Apple” the company), and polysemy (same tag with multiple related meanings, e.g., “to get” meaning either “to take” or “to understand”). Noise may also result from misspelled tags or the like, and/or from semantically-empty tags (tags that provide little or no useful identification (e.g., “image”, “photo”, “nice”, “cool”, etc. are examples of semantically-empty tags for a picture of a dog or some such thing). Such noise may reduce the efficiency and accuracy of media indexing, searching, and the like. Automatic methods of tagging have proven to be quite noisy. Social tagging can also result in noise, but is generally more accurate than automated tagging.
Semantic loss with respect to media tagging generally refers to meaning or elements in the media that are not explicitly identified by tags. That is, when some element in an image is overlooked such that no corresponding tag is provided, semantic loss results. For example, given an image of a car, tags may be provided including “car”, “vehicle”, and “auto”. But if no tag for “tires” is provided and the image includes tires, then semantic loss results. In this example, a search on “tires” would not produce the image even though it includes tires.
To minimize noise and compensate for semantic loss, technologies for tag recommendation such as those described herein may be used that improve the quality of social tagging.